%load_ext pretty_jupyter
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from scipy.stats import chi2_contingency
from collections import Counter
from utils import *

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import confusion_matrix
from sklearn.linear_model import LogisticRegression
from sklearn.linear_model import LogisticRegression
from sklearn.svm import LinearSVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
import shap

filename = "./O_G_Equipment_Data.xlsx"
data = pd.read_excel(filename)

data.head()

print(data.info())

print(f"The equipment has failed {data['Fail'].value_counts().iloc[1]} times")
print(f"The failure rate is {data['Fail'].value_counts().iloc[1]/len(data)*100:.2f}%")
fails_conts = data['Fail'].value_counts()

labels = ['False', 'True']
counts = [fails_conts[False], fails_conts[True]]

fig, ax = plt.subplots()
ax.bar(labels, counts, color=['green', 'red'])

ax.set_ylabel('Count')
ax.set_title('Comparison of Fail and Not Fail Equipment')

for i in range(len(counts)):
    ax.text(i, counts[i] + 1, str(counts[i]), ha='center')
ax.grid()

plt.savefig('fail_not_fail.png', dpi=300 , bbox_inches='tight')

fails_per_preset_1 = data.groupby(['Preset_1', 'Fail']).size().unstack().sort_values(by=True, ascending=False)
fails_per_preset_2 = data.groupby(['Preset_2', 'Fail']).size().unstack().sort_values(by=True, ascending=False)

combination = data.groupby(['Preset_1','Preset_2', 'Fail']).size().unstack(fill_value=0)
combination['Percentage_fail'] = combination[True] / (combination[True] + combination[False]) * 100
combination['cumulative'] = combination[True].cumsum()
combination.reset_index(inplace=True)
combination.reset_index(inplace=True)

combination_sorted =combination.sort_values(by=True, ascending=False)
combination_sorted

parameters = ['Temperature', 'Pressure','VibrationX', 'VibrationY', 'VibrationZ', 'Frequency']
fig, ax = plt.subplots(3, 2, figsize=(15, 15))
axs = ax.T.flatten()
for axx in range(len(axs)):
    g = sns.histplot(data, x=parameters[axx], kde=True, element='step', legend=True, ax=axs[axx], common_norm=False)
    leg = g.axes.get_legend()
    axs[axx].set_xlabel(parameters[axx])
    axs[axx].set_ylabel('Count')
    new_title = 'Fail'
plt.savefig('histograms.png', dpi=300, bbox_inches='tight')

fig, ax = plt.subplots(3, 2, figsize=(15, 15))

axs = ax.T.flatten()

for axx in range(len(axs)):
    g = sns.histplot(data, x=parameters[axx], hue='Fail', kde=True, element='step', legend=True,stat='density', ax=axs[axx], common_norm=False)
    leg = g.axes.get_legend()
    axs[axx].set_xlabel(parameters[axx])
    axs[axx].set_ylabel('Density')
    new_title = 'Fail'
    leg.set_title(new_title)
    new_labels = ['False', 'True']
    for t, l in zip(leg.texts, new_labels):
        t.set_text(l)

plt.savefig('histograms_separated.png', dpi=300, bbox_inches='tight')

newDF = oversamplig_dataframe(data, 'Fail', method='ADASYN')

X = newDF.drop(columns=['Cycle','Preset_1', 'Preset_2', 'Fail'])
y = newDF.drop(columns=['Cycle', 'Preset_1', 'Preset_2', 'Temperature', 'Pressure', 'VibrationX', 'VibrationY', 'VibrationZ', 'Frequency'])

X_train, X_test, y_train, y_test = train_test_split(X, y , test_size=0.20, random_state=0)

ss_train = StandardScaler()
X_train = ss_train.fit_transform(X_train)

ss_test = StandardScaler()
X_test = ss_test.fit_transform(X_test)

model = LogisticRegression()

model.fit(X_train, y_train)
predictions = model.predict(X_test)

cm = confusion_matrix(y_test, predictions)

TN, FP, FN, TP = confusion_matrix(y_test, predictions).ravel()

print('True Positive(TP)  = ', TP)
print('False Positive(FP) = ', FP)
print('True Negative(TN)  = ', TN)
print('False Negative(FN) = ', FN)

accuracy =  (TP + TN) / (TP + FP + TN + FN)

print('Accuracy of the binary classifier = {:0.3f}'.format(accuracy))

X_test_df = pd.DataFrame(X_test, columns=X.columns)
explainer = shap.Explainer(model.predict, X_test)
shap_values = explainer(X_test_df)
shap.summary_plot(shap_values, plot_type='violin', show=False)
plt.savefig('shap_summary_plot.png', dpi=300, bbox_inches='tight')

models = {}

# Logistic Regression
models['Logistic Regression'] = LogisticRegression()
# Support Vector Machines
models['Support Vector Machines'] = LinearSVC()
# Decision Trees
models['Decision Trees'] = DecisionTreeClassifier()
# Random Forest
models['Random Forest'] = RandomForestClassifier()
# Naive Bayes
models['Naive Bayes'] = GaussianNB()
# K-Nearest Neighbors
models['K-Nearest Neighbor'] = KNeighborsClassifier()

from sklearn.metrics import accuracy_score, precision_score, recall_score

accuracy, precision, recall = {}, {}, {}

for key in models.keys():
    
    # Fit the classifier
    models[key].fit(X_train, y_train)
    
    # Make predictions
    predictions = models[key].predict(X_test)
    
    # Calculate metrics
    accuracy[key] = accuracy_score(predictions, y_test)
    precision[key] = precision_score(predictions, y_test)
    recall[key] = recall_score(predictions, y_test)

import pandas as pd

df_model = pd.DataFrame(index=models.keys(), columns=['Accuracy', 'Precision', 'Recall'])
df_model['Accuracy'] = accuracy.values()
df_model['Precision'] = precision.values()
df_model['Recall'] = recall.values()
df_model

ax = df_model.plot.barh()
ax.legend(
    ncol=len(models.keys()), 
    bbox_to_anchor=(0, 1), 
    loc='lower left', 
    prop={'size': 14}
)
plt.tight_layout()
plt.savefig('model_comparison.png', dpi=300, bbox_inches='tight')

falhas = data[data['Fail'] == True]['Cycle']
fig, axs = plt.subplots(6, 1, figsize=(18, 20), sharex=True, dpi=200)
axs[0].plot(data['Cycle'], data['VibrationX'], label='VibrationX', color='blue')
axs[1].plot(data['Cycle'], data['VibrationY'], label='VibrationY', color='red')
axs[2].plot(data['Cycle'], data['VibrationZ'], label='VibrationZ', color='green')
axs[3].plot(data['Cycle'], data['Temperature'], label='VibrationX', color='black')
axs[4].plot(data['Cycle'], data['Pressure'], label='VibrationX', color='gray')
axs[5].plot(data['Cycle'], data['Frequency'], label='Frequency', color='gray')


for ax in axs:
    for ciclo in falhas:
        ax.axvline(x=ciclo, color='k', linestyle='--', linewidth=1)

axs[0].set_ylabel('VibrationX')
axs[1].set_ylabel('VibrationY')
axs[2].set_ylabel('VibrationZ')
axs[3].set_ylabel('Temperature')
axs[4].set_ylabel('Pressure')
axs[4].set_xlabel('Cycle')
axs[5].set_xlabel('Frequency')

axs[0].set_title('VibrationX')
axs[1].set_title('VibrationY')
axs[2].set_title('VibrationZ')
axs[3].set_title('Temperature')
axs[4].set_title('Pressure')
axs[5].set_title('Frequency')


plt.savefig('Vibration_Analysis.png', dpi=200, bbox_inches='tight')

import numpy as np
import pywt
import matplotlib.pyplot as plt

# Selecionar as componentes para análise
vibration_x = data['VibrationX'].values
vibration_y = data['VibrationY'].values
vibration_z = data['VibrationZ'].values
pressure = data['Pressure'].values
temperature = data['Temperature'].values

scales = np.arange(1, 128)

coef_x, freqs_x = pywt.cwt(vibration_x, scales, 'cmor1.5-0.5')
coef_y, freqs_y = pywt.cwt(vibration_y, scales, 'cmor1.5-0.5')
coef_z, freqs_z = pywt.cwt(vibration_z, scales, 'cmor1.5-0.5')
coef_pres, freqs_pres = pywt.cwt(pressure, scales, 'cmor1.5-0.5')
coef_temp, freqs_temp = pywt.cwt(temperature, scales, 'cmor1.5-0.5')

fig, axs = plt.subplots(5, 1, figsize=(16, 14), sharex=True)
cax1 = axs[0].pcolormesh(np.arange(len(vibration_x)), freqs_x, np.abs(coef_x), cmap='plasma')
fig.colorbar(cax1, ax=axs[0])
axs[0].set_title('VibrationX')
axs[0].set_ylabel('Frequency')
axs[0].set_yscale('log', base=2)

cax2 = axs[1].pcolormesh(np.arange(len(vibration_y)), freqs_y, np.abs(coef_y), cmap='plasma')
fig.colorbar(cax2, ax=axs[1])
axs[1].set_title('VibrationY')
axs[1].set_ylabel('Frequency')
axs[1].set_yscale('log', base=2)

cax3 = axs[2].pcolormesh(np.arange(len(vibration_z)), freqs_z, np.abs(coef_z), cmap='plasma')
fig.colorbar(cax3, ax=axs[2])
axs[2].set_title('VibrationZ')
axs[2].set_ylabel('Frequency')
axs[2].set_yscale('log', base=2)

cax4 = axs[3].pcolormesh(np.arange(len(pressure)), freqs_pres, np.abs(coef_pres), cmap='plasma')
fig.colorbar(cax4, ax=axs[3])
axs[3].set_title('Pressure')
axs[3].set_ylabel('Frequency')
axs[3].set_yscale('log', base=2)

cax5 = axs[4].pcolormesh(np.arange(len(temperature)), freqs_temp, np.abs(coef_temp), cmap='plasma')
fig.colorbar(cax5, ax=axs[4])
axs[4].set_title('Temperature')
axs[4].set_ylabel('Frequency')
axs[4].set_yscale('log', base=2)

axs[4].set_xlabel('Cycle')

plt.tight_layout()
plt.savefig('wavelet_Analysis.png', dpi=200, bbox_inches='tight')

pywt.scale2frequency('cmor1.5-0.5',scales)